{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination.MANUSCRIPT.06-02-2022.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 2 Jun 2022, 18:02:35
{txt}
{com}.    
.    
. 
. use "C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination_Dataset_06-02-2022.dta", replace 
{txt}
{com}. 
. 
. 
.   
. 
. ****  2022 PAR DATA REPLICATION [6/2/2022]: "UNDERSTANDING ORGANIZATIONAL SUSCEPTIBILITY TO AGE DISCIMINRATION WITHIN THE U.S. FEDERAL WORKFORCE" [KRAUSE & PARK] ****
. 
.  
.    
.    
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
.    
. 
. 
. 
. 
. *** FIGURE 1: CREATE VERTICAL BOX-WHISKER PLOTS PER YEAR THAT CAPTURES THE DISTRIBUTION OF AGE DISCIRMINATION FORMAL COMPLAINTS ACROSS U.S.FEDERAL AGENCIES **
. ***           ONE VERTICAL BOX-WHISKER PLOT PER YEAR IN SAMPLE -- 10 YEARS--> 10 BOX-WHISKER VERTICAL PLOTS  
. 
. graph set window fontface "Century Schoolbook"
{txt}
{com}. set scheme sj, permanently
{txt}({cmd:set scheme} preference recorded)

{com}. graph bar (sum) age_discrimination, over (year, label(labsize(small))) ylabel (0(500)5000, labsize (small) angle(horizon)) blabel (total, position(outside) format (%9.0gc)) ytitle("") scheme(sj)
{res}{txt}
{com}. 
. 
. 
. *** TABLE 1: COMPARISON OF BETWEEN TO WITHIN VARIATION IN ANALYZING AGE DISCRIMINATION FORMAL COMPLAINTS IN U.S. FEDERAL AGENCIES ***
. 
. 
. xtset a_id year, yearly
{res}{txt}{col 8}panel variable:  {res}a_id (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 2010 to 2019, but with gaps
{txt}{col 17}delta:  {res}1 year
{txt}
{com}. 
. xtsum age_discrimination  ratio_40over_suplb  ratio_40over_nonsuplb   ratio40suplb_nonsuplb   orgjustice_sem  nonprof40over_tr_lb  prof_nonprof_ratiolb_40over ///
> politicization_lb   ratio_fsup_msup ratio_minsup_nonmsup lntwf

{txt}Variable         {c |}      Mean   Std. Dev.       Min        Max {c |}    Observations
{hline 17}{c +}{hline 44}{c +}{hline 16}
age_di~n{col 10}overall {c |} {res}  32.2029   72.50792          0        887{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} 64.70322          0    561.625{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} 15.78544   -98.4221   357.5779{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
r~_suplb{col 10}overall {c |} {res} .8756021   .0641863    .545455    .987805{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .0634058     .59375   .9673505{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0238252   .6474421   1.018654{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
ratio_..{col 10}overall {c |} {res} .6835453   .0897468    .355567    .939189{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res}  .087751   .4409685   .9223505{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0252637   .5522691   .8245991{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
ratio4~b{col 10}overall {c |} {res} 1.298518   .1624386   .7379679   2.501044{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .1552002   .7744566   1.986511{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0620794   1.041343   1.813051{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
orgjus~m{col 10}overall {c |} {res} .0747353   .1685507   -.487838     .64481{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .1540227  -.2942685   .5244455{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0688236  -.1659204   .3404443{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
nonpro~b{col 10}overall {c |} {res} .5173535   .1649136    .083909     .91954{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res}  .163884   .0909503    .912284{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0191482   .4085827     .64255{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
prof_n~r{col 10}overall {c |} {res} .5898478   .8250387          0    6.15424{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .7447416          0   5.517732{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res}  .075803  -.1531442   1.226356{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
politi~b{col 10}overall {c |} {res} .0220929    .051625          0      .5455{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .0652461          0   .4431667{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0123276  -.1076738   .1244262{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
ra~_msup{col 10}overall {c |} {res} .7207612   .4246768      .1414       2.76{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .4090175      .1588   2.120914{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0799147   .0813469   1.437428{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
ra~nmsup{col 10}overall {c |} {res} .5805867   1.002242    .044776     9.0297{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} .8618979   .0621833   8.089517{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0988556  -.8493603    1.52077{txt} {c |} T-bar =     6.9
{col 18}{c |}{col 63}{c |}
lntwf{col 10}overall {c |} {res} 8.672307   1.497073   4.804021     12.743{txt} {c |}{col 69}N =     897
{col 10}between {c |}{col 31}{res} 1.631839   4.804021   12.63477{txt} {c |}{col 69}n =     130
{col 10}within  {c |}{col 31}{res} .0760569   7.899612   8.999011{txt} {c |} T-bar =     6.9

{com}. 
. 
. ** EVALUATE DIFFERENCES IN MEANS BETWEEN PROPORTION OF WOMEN SUPERVISORS (TO ALL SUPERVISORS) AND MINORITY SUPERVISORS (TO ALL SUPERVISORS) ***
. 
. ttest ratiofemsup_sup == ratiominsup_sup, unpaired unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
ratiof~p {c |}{res}{col 12}    897{col 22} .3879462{col 34} .0043278{col 46} .1296175{col 58} .3794524{col 70}   .39644
{txt}ratiom~p {c |}{res}{col 12}    897{col 22} .2946848{col 34} .0046997{col 46} .1407568{col 58}  .285461{col 70} .3039085
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  1,794{col 22} .3413155{col 34} .0033781{col 46} .1430804{col 58} .3346901{col 70} .3479409
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0932615{col 34} .0063888{col 58}  .080731{col 70} .1057919
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}ratiofemsup_sup{txt}) - mean({res}ratiominsup_sup{txt})          t = {res} 14.5975
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 1779.96

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. 
. 
. *** OBTAIN BETWEEN-EFFECTS [AGENCY GROUP MEANS] DESCRIPTIVE STATISTICS FOR EVALUATING MARGINAL EFFECTS FOR KEY COVARIATES IN REGRESSION MODELS **
. 
. collapse age_discrimination ratio_40over_suplb  ratio_40over_nonsuplb  ratio40suplb_nonsuplb      orgjustice_sem  nonprof40over_tr_lb  ///
> politicization_lb   ratio_fsup_msup ratio_minsup_nonmsup lntwf, by(a_id)
{txt}
{com}. 
. 
. sum ratio_40over_suplb  ratio_40over_nonsuplb  ratio40suplb_nonsuplb       orgjustice_sem   nonprof40over_tr_lb   politicization_lb   ratio_fsup_msup ratio_minsup_nonmsup lntwf, detail

                  {txt}(mean) ratio_40over_suplb
{hline 61}
      Percentiles      Smallest
 1%    {res} .7136055         .59375
{txt} 5%    {res} .7457415       .7136055
{txt}10%    {res} .7818404       .7295974       {txt}Obs         {res}        130
{txt}25%    {res} .8472625       .7329078       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .8903902                      {txt}Mean          {res} .8747578
                        {txt}Largest       Std. Dev.     {res} .0634058
{txt}75%    {res} .9165678       .9544995
{txt}90%    {res} .9427873       .9627305       {txt}Variance      {res} .0040203
{txt}95%    {res} .9487309        .965653       {txt}Skewness      {res}-1.276586
{txt}99%    {res}  .965653       .9673505       {txt}Kurtosis      {res} 5.189739

                {txt}(mean) ratio_40over_nonsuplb
{hline 61}
      Percentiles      Smallest
 1%    {res} .4565813       .4409685
{txt} 5%    {res} .5278292       .4565813
{txt}10%    {res} .5602604       .4999451       {txt}Obs         {res}        130
{txt}25%    {res} .6398479       .5090092       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .6944372                      {txt}Mean          {res} .6889954
                        {txt}Largest       Std. Dev.     {res}  .087751
{txt}75%    {res} .7528171       .8337139
{txt}90%    {res}  .792173       .8431226       {txt}Variance      {res} .0077002
{txt}95%    {res} .8181308       .8690926       {txt}Skewness      {res}-.3591871
{txt}99%    {res} .8690926       .9223505       {txt}Kurtosis      {res} 3.058442

                {txt}(mean) ratio40suplb_nonsuplb
{hline 61}
      Percentiles      Smallest
 1%    {res}  .974542       .7744566
{txt} 5%    {res} 1.090932        .974542
{txt}10%    {res} 1.124221       .9806453       {txt}Obs         {res}        130
{txt}25%    {res} 1.184185       1.053038       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} 1.265506                      {txt}Mean          {res} 1.287502
                        {txt}Largest       Std. Dev.     {res} .1552002
{txt}75%    {res} 1.367779       1.575457
{txt}90%    {res} 1.474348       1.619215       {txt}Variance      {res} .0240871
{txt}95%    {res} 1.564657       1.691939       {txt}Skewness      {res} .7108519
{txt}99%    {res} 1.691939       1.986511       {txt}Kurtosis      {res} 5.888232

                    {txt}(mean) orgjustice_sem
{hline 61}
      Percentiles      Smallest
 1%    {res}-.2471823      -.2942685
{txt} 5%    {res} -.184525      -.2471823
{txt}10%    {res}-.1089839      -.2406142       {txt}Obs         {res}        130
{txt}25%    {res}-.0171223       -.214706       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .0596354                      {txt}Mean          {res} .0772725
                        {txt}Largest       Std. Dev.     {res} .1540227
{txt}75%    {res}  .174324       .4237997
{txt}90%    {res} .2659022       .4466526       {txt}Variance      {res}  .023723
{txt}95%    {res} .3710567       .4631644       {txt}Skewness      {res} .2972123
{txt}99%    {res} .4631644       .5244455       {txt}Kurtosis      {res} 3.223264

                 {txt}(mean) nonprof40over_tr_lb
{hline 61}
      Percentiles      Smallest
 1%    {res} .1442743       .0909503
{txt} 5%    {res} .2378629       .1442743
{txt}10%    {res}    .2907       .1679444       {txt}Obs         {res}        130
{txt}25%    {res} .3997402        .204084       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .5466054                      {txt}Mean          {res} .5258724
                        {txt}Largest       Std. Dev.     {res}  .163884
{txt}75%    {res} .6524423       .7884546
{txt}90%    {res} .7233644       .8032876       {txt}Variance      {res}  .026858
{txt}95%    {res} .7835125       .8125982       {txt}Skewness      {res}-.3505535
{txt}99%    {res} .8125982        .912284       {txt}Kurtosis      {res} 2.558231

                  {txt}(mean) politicization_lb
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        130
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .0016007                      {txt}Mean          {res} .0319381
                        {txt}Largest       Std. Dev.     {res} .0652461
{txt}75%    {res}   .03025          .2188
{txt}90%    {res}  .095725       .2411333       {txt}Variance      {res} .0042571
{txt}95%    {res}    .1789          .2607       {txt}Skewness      {res} 3.251849
{txt}99%    {res}    .2607       .4431667       {txt}Kurtosis      {res}  16.2115

                   {txt}(mean) ratio_fsup_msup
{hline 61}
      Percentiles      Smallest
 1%    {res} .1636125          .1588
{txt} 5%    {res}    .2761       .1636125
{txt}10%    {res}   .30399         .21694       {txt}Obs         {res}        130
{txt}25%    {res}   .42255         .24415       {txt}Sum of Wgt. {res}        130

{txt}50%    {res}   .64529                      {txt}Mean          {res}  .736688
                        {txt}Largest       Std. Dev.     {res} .4090175
{txt}75%    {res}   .95914        1.66838
{txt}90%    {res} 1.288846       1.725857       {txt}Variance      {res} .1672953
{txt}95%    {res}     1.61       1.911075       {txt}Skewness      {res} 1.004626
{txt}99%    {res} 1.911075       2.120914       {txt}Kurtosis      {res} 3.559104

                 {txt}(mean) ratio_minsup_nonmsup
{hline 61}
      Percentiles      Smallest
 1%    {res}  .100594       .0621833
{txt} 5%    {res} .1555485        .100594
{txt}10%    {res} .1818917       .1007868       {txt}Obs         {res}        130
{txt}25%    {res}  .272078        .114452       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} .3575966                      {txt}Mean          {res}  .527939
                        {txt}Largest       Std. Dev.     {res} .8618979
{txt}75%    {res}  .506353       1.379818
{txt}90%    {res} .8234243       3.824309       {txt}Variance      {res} .7428679
{txt}95%    {res} .9337344       4.959243       {txt}Skewness      {res} 6.742897
{txt}99%    {res} 4.959243       8.089517       {txt}Kurtosis      {res} 53.42954

                        {txt}(mean) lntwf
{hline 61}
      Percentiles      Smallest
 1%    {res} 4.948735       4.804021
{txt} 5%    {res}  5.35932       4.948735
{txt}10%    {res} 6.220908       5.111334       {txt}Obs         {res}        130
{txt}25%    {res} 7.100842       5.173221       {txt}Sum of Wgt. {res}        130

{txt}50%    {res} 8.189445                      {txt}Mean          {res} 8.206713
                        {txt}Largest       Std. Dev.     {res} 1.631839
{txt}75%    {res} 9.340635       12.04009
{txt}90%    {res} 10.08836       12.22137       {txt}Variance      {res}   2.6629
{txt}95%    {res} 11.03362       12.47689       {txt}Skewness      {res} .2757472
{txt}99%    {res} 12.47689       12.63477       {txt}Kurtosis      {res} 2.929204
{txt}
{com}. *
. *
. *
. *
. 
. save "C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\collapsedagediscrim1.06-02-2022.dta", replace 
{txt}file C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\collapsedagediscrim1.06-02-2022.dta saved

{com}. *
. *
. *
. use "C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination_Dataset_06-02-2022.dta", replace 
{txt}
{com}. 
. 
. 
. 
. xtset a_id year, yearly
{res}{txt}{col 8}panel variable:  {res}a_id (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 2010 to 2019, but with gaps
{txt}{col 17}delta:  {res}1 year
{txt}
{com}. 
. 
. 
. 
. 
. *** TABLE 2: REGRESSION MODEL TABLE PREDICTING VARIATIONS IN AGE DISCRIMINATION FORMAL COMPLAINTS IN U.S. FEDERAL AGENCIES ***
. 
. 
. ** ratio_4054_totallb: Lower bound for the ratio of older employees (age of 40-54) to total
. ** ratio_55over_totallb: Lower bound for the ratio of older employees (55 or older) to total
. 
. 
. ** MODEL 1: DISAGGREGATE SUPERVISOR/SUBORDINATE RATIO MEASURES [ratio_40over_suplb; ratio_40over_nonsuplb]: ONLY RANDOM INTERCEPT MODEL SPECIFICATION WITH BE & WE ESTIMATES FOR ALL COVARIATES [SANS YEAR UNIT EFFECTS & LN(TOTAL WORKFORCE)] --- REDUCED MODEL: ONLY CONTROL COVARIATES ARE LNTEF & YEAR UNIT EFFECTS ***
. 
. xthybrid age_discrimination  ratio_40over_suplb  ratio_40over_nonsuplb   lntwf ///
> yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10  ratio_55over_totallb, clusterid(a_id) vce(cluster a_id)  family(nbinomial) link(log) full ///
> use(ratio_40over_suplb  ratio_40over_nonsuplb  ratio_55over_totallb) 

{res}
{txt}{hline}
{p 0 8}Model {hi:model}{p_end}
{hline}

{txt}Mixed-effects GLM{col 49}{txt}Number of obs{col 67}={res}{col 69}       897
{txt}Family: {col 15}{res}negative binomial
{txt}Link: {col 29}{res}log
{txt}Overdispersion: {col 28}{res}mean
{txt}Group variable: {col 28}{res}a_id{col 49}{txt}Number of groups{col 67}={res}{col 69}       130

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       6.9
{col 63}{txt}max{col 67}={res}{col 69}        10

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}  1005.14
{txt}Log pseudolikelihood = {res}-2761.0552{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. Err. adjusted for {res:130} clusters in a_id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}age_discri~n{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}R__yr2 {c |}{col 14}{res}{space 2}-.0174508{col 26}{space 2} .0482695{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.1120573{col 67}{space 3} .0771557
{txt}{space 6}R__yr3 {c |}{col 14}{res}{space 2}-.0262367{col 26}{space 2} .0561041{col 37}{space 1}   -0.47{col 46}{space 3}0.640{col 54}{space 4}-.1361987{col 67}{space 3} .0837252
{txt}{space 6}R__yr4 {c |}{col 14}{res}{space 2} -.090288{col 26}{space 2} .0643714{col 37}{space 1}   -1.40{col 46}{space 3}0.161{col 54}{space 4}-.2164537{col 67}{space 3} .0358776
{txt}{space 6}R__yr5 {c |}{col 14}{res}{space 2}-.1049175{col 26}{space 2}  .075717{col 37}{space 1}   -1.39{col 46}{space 3}0.166{col 54}{space 4}-.2533202{col 67}{space 3} .0434851
{txt}{space 6}R__yr6 {c |}{col 14}{res}{space 2} -.020527{col 26}{space 2} .0708044{col 37}{space 1}   -0.29{col 46}{space 3}0.772{col 54}{space 4}-.1593011{col 67}{space 3} .1182471
{txt}{space 6}R__yr7 {c |}{col 14}{res}{space 2} .0343288{col 26}{space 2} .0726651{col 37}{space 1}    0.47{col 46}{space 3}0.637{col 54}{space 4}-.1080922{col 67}{space 3} .1767499
{txt}{space 6}R__yr8 {c |}{col 14}{res}{space 2}-.0682184{col 26}{space 2}  .073501{col 37}{space 1}   -0.93{col 46}{space 3}0.353{col 54}{space 4}-.2122778{col 67}{space 3}  .075841
{txt}{space 6}R__yr9 {c |}{col 14}{res}{space 2}-.0221266{col 26}{space 2} .0779251{col 37}{space 1}   -0.28{col 46}{space 3}0.776{col 54}{space 4}-.1748569{col 67}{space 3} .1306037
{txt}{space 5}R__yr10 {c |}{col 14}{res}{space 2} -.059184{col 26}{space 2} .0883832{col 37}{space 1}   -0.67{col 46}{space 3}0.503{col 54}{space 4}-.2324118{col 67}{space 3} .1140438
{txt}{space 4}R__lntwf {c |}{col 14}{res}{space 2} .8651921{col 26}{space 2} .0311477{col 37}{space 1}   27.78{col 46}{space 3}0.000{col 54}{space 4} .8041438{col 67}{space 3} .9262404
{txt}W__ra~_suplb {c |}{col 14}{res}{space 2}-.2269339{col 26}{space 2} 1.086649{col 37}{space 1}   -0.21{col 46}{space 3}0.835{col 54}{space 4}-2.356726{col 67}{space 3} 1.902858
{txt}W__ra~nsuplb {c |}{col 14}{res}{space 2} .5784907{col 26}{space 2} .9034439{col 37}{space 1}    0.64{col 46}{space 3}0.522{col 54}{space 4}-1.192227{col 67}{space 3} 2.349208
{txt}W__ratio_5~b {c |}{col 14}{res}{space 2}  1.86516{col 26}{space 2} .9964847{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4}-.0879147{col 67}{space 3} 3.818234
{txt}B__ra~_suplb {c |}{col 14}{res}{space 2}-2.990743{col 26}{space 2} .8077069{col 37}{space 1}   -3.70{col 46}{space 3}0.000{col 54}{space 4} -4.57382{col 67}{space 3}-1.407667
{txt}B__ra~nsuplb {c |}{col 14}{res}{space 2} 2.657318{col 26}{space 2} 1.095393{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .5103875{col 67}{space 3} 4.804248
{txt}B__ratio_5~b {c |}{col 14}{res}{space 2}-.1792901{col 26}{space 2} 1.043734{col 37}{space 1}   -0.17{col 46}{space 3}0.864{col 54}{space 4} -2.22497{col 67}{space 3}  1.86639
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.190138{col 26}{space 2} .7758298{col 37}{space 1}   -5.40{col 46}{space 3}0.000{col 54}{space 4}-5.710736{col 67}{space 3}-2.669539
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-3.207836{col 26}{space 2} .1821609{col 54}{space 4}-3.564865{col 67}{space 3}-2.850808
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}a_id        {col 14}{txt}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1963683{col 26}{space 2} .0347735{col 54}{space 4} .1387836{col 67}{space 3} .2778463
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay model:model}}{col 14}{c |}{res}{col 16}       897{col 27}        .{col 38}-2761.055{col 49}    19{col 58}  5560.11{col 69} 5651.292
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. 
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECTS BASED ON BETWEEN EFFECTS INTERDECILE CHANGES IN RESPECTIVE COVARIATES ****
. 
. lincom _b[B__ratio_40over_suplb]*0.9427874  -  _b[B__ratio_40over_suplb]*0.7818404, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.160947{res}*{res}[age_discrimination]B__ratio_40over_suplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6179479{col 26}{space 2}  .080332{col 37}{space 1}   -3.70{col 46}{space 3}0.000{col 54}{space 4} .4789579{col 67}{space 3} .7972717
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom _b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.2319127{res}*{res}[age_discrimination]B__ratio_40over_nonsuplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.851999{col 26}{space 2} .4704735{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} 1.125655{col 67}{space 3} 3.047026
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECT ABSOLUTE DIFFERENCE [BASED ON INTERDECILE CHANGES] BETWEEN THESE RESPECTIVE COVARIATES ON THE INCIDENCE OF AGE DISCRIMINATION FORMAL COMPLAINTS  ****
. 
. testnl  abs(_b[B__ratio_40over_suplb]*0.9427874  -  _b[B__ratio_40over_suplb]*0.7818404) = abs(_b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603)

{col 3}{txt}(1){res}{col 8}{bind:abs(_b[B__ratio_40over_suplb]*0.9427874 - _b[B__ratio_40over_suplb]*0.7818404) = abs(_b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603)}

{txt}{ralign 22:chi2({res:1})} =  {res}      0.33
{txt}{ralign 22:Prob > chi2} =  {res}      0.5685{txt}

{com}. 
. *
. 
. *
. *
. *
. *
. 
. ** MODEL 2: DISAGGREGATE SUPERVISOR/SUBORDINATE RATIO MEASURES [ratio_40over_suplb; ratio_40over_nonsuplb]: ONLY RANDOM INTERCEPT MODEL SPECIFICATION WITH BE & WE ESTIMATES FOR ALL COVARIATES [SANS YEAR UNIT EFFECTS & LN(TOTAL WORKFORCE)] --- FULL MODEL: ALL CONTROL COVARIATES ARE LNTEF & YEAR UNIT EFFECTS ***
. 
. xthybrid age_discrimination  ratio_40over_suplb  ratio_40over_nonsuplb   orgjustice_sem   nonprof40over_tr_lb   politicization_lb ratio_fsup_msup ratio_minsup_nonmsup lntwf ///
> yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10  ratio_55over_totallb, clusterid(a_id) vce(cluster a_id)  family(nbinomial) link(log) full ///
> use(ratio_40over_suplb  ratio_40over_nonsuplb    orgjustice_sem  nonprof40over_tr_lb   politicization_lb   ratio_fsup_msup ratio_minsup_nonmsup ratio_55over_totallb) 

{res}
{txt}{hline}
{p 0 8}Model {hi:model}{p_end}
{hline}

{txt}Mixed-effects GLM{col 49}{txt}Number of obs{col 67}={res}{col 69}       897
{txt}Family: {col 15}{res}negative binomial
{txt}Link: {col 29}{res}log
{txt}Overdispersion: {col 28}{res}mean
{txt}Group variable: {col 28}{res}a_id{col 49}{txt}Number of groups{col 67}={res}{col 69}       130

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       6.9
{col 63}{txt}max{col 67}={res}{col 69}        10

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}26{txt}){col 67}={res}{col 70}  1423.52
{txt}Log pseudolikelihood = {res}-2738.6103{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. Err. adjusted for {res:130} clusters in a_id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}age_discri~n{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}R__yr2 {c |}{col 14}{res}{space 2}-.0084347{col 26}{space 2} .0471816{col 37}{space 1}   -0.18{col 46}{space 3}0.858{col 54}{space 4} -.100909{col 67}{space 3} .0840395
{txt}{space 6}R__yr3 {c |}{col 14}{res}{space 2}-.0124917{col 26}{space 2} .0531032{col 37}{space 1}   -0.24{col 46}{space 3}0.814{col 54}{space 4}-.1165721{col 67}{space 3} .0915888
{txt}{space 6}R__yr4 {c |}{col 14}{res}{space 2}-.0680979{col 26}{space 2} .0634829{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4} -.192522{col 67}{space 3} .0563263
{txt}{space 6}R__yr5 {c |}{col 14}{res}{space 2}-.0747859{col 26}{space 2} .0760281{col 37}{space 1}   -0.98{col 46}{space 3}0.325{col 54}{space 4}-.2237983{col 67}{space 3} .0742265
{txt}{space 6}R__yr6 {c |}{col 14}{res}{space 2} .0126631{col 26}{space 2} .0713037{col 37}{space 1}    0.18{col 46}{space 3}0.859{col 54}{space 4}-.1270895{col 67}{space 3} .1524157
{txt}{space 6}R__yr7 {c |}{col 14}{res}{space 2} .0662091{col 26}{space 2} .0744048{col 37}{space 1}    0.89{col 46}{space 3}0.374{col 54}{space 4}-.0796217{col 67}{space 3} .2120399
{txt}{space 6}R__yr8 {c |}{col 14}{res}{space 2}-.0352584{col 26}{space 2} .0772996{col 37}{space 1}   -0.46{col 46}{space 3}0.648{col 54}{space 4}-.1867628{col 67}{space 3} .1162459
{txt}{space 6}R__yr9 {c |}{col 14}{res}{space 2} .0182601{col 26}{space 2} .0798159{col 37}{space 1}    0.23{col 46}{space 3}0.819{col 54}{space 4}-.1381762{col 67}{space 3} .1746964
{txt}{space 5}R__yr10 {c |}{col 14}{res}{space 2}-.0110554{col 26}{space 2}  .090242{col 37}{space 1}   -0.12{col 46}{space 3}0.902{col 54}{space 4}-.1879265{col 67}{space 3} .1658157
{txt}{space 4}R__lntwf {c |}{col 14}{res}{space 2} .8438505{col 26}{space 2} .0313368{col 37}{space 1}   26.93{col 46}{space 3}0.000{col 54}{space 4} .7824315{col 67}{space 3} .9052695
{txt}W__ra~_suplb {c |}{col 14}{res}{space 2}-.4747602{col 26}{space 2} 1.147205{col 37}{space 1}   -0.41{col 46}{space 3}0.679{col 54}{space 4} -2.72324{col 67}{space 3}  1.77372
{txt}W__ra~nsuplb {c |}{col 14}{res}{space 2} .1778184{col 26}{space 2} 1.181403{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4} -2.13769{col 67}{space 3} 2.493327
{txt}W__orgjust~m {c |}{col 14}{res}{space 2} .0856739{col 26}{space 2} .2536355{col 37}{space 1}    0.34{col 46}{space 3}0.736{col 54}{space 4}-.4114425{col 67}{space 3} .5827904
{txt}W__nonprof~b {c |}{col 14}{res}{space 2} .6974492{col 26}{space 2} 1.430274{col 37}{space 1}    0.49{col 46}{space 3}0.626{col 54}{space 4}-2.105836{col 67}{space 3} 3.500734
{txt}W__politic~b {c |}{col 14}{res}{space 2}-1.310077{col 26}{space 2} 1.517751{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4}-4.284814{col 67}{space 3} 1.664659
{txt}W__ratio_f~p {c |}{col 14}{res}{space 2} .1760072{col 26}{space 2} .2391431{col 37}{space 1}    0.74{col 46}{space 3}0.462{col 54}{space 4}-.2927046{col 67}{space 3}  .644719
{txt}W__ratio_m~p {c |}{col 14}{res}{space 2}-.4036482{col 26}{space 2} .0973242{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-.5944002{col 67}{space 3}-.2128962
{txt}W__ratio_5~b {c |}{col 14}{res}{space 2} 1.749247{col 26}{space 2} .9965581{col 37}{space 1}    1.76{col 46}{space 3}0.079{col 54}{space 4}-.2039715{col 67}{space 3} 3.702465
{txt}B__ra~_suplb {c |}{col 14}{res}{space 2}-1.971627{col 26}{space 2} .7875314{col 37}{space 1}   -2.50{col 46}{space 3}0.012{col 54}{space 4} -3.51516{col 67}{space 3}-.4280936
{txt}B__ra~nsuplb {c |}{col 14}{res}{space 2} 1.765438{col 26}{space 2} .9471304{col 37}{space 1}    1.86{col 46}{space 3}0.062{col 54}{space 4}-.0909038{col 67}{space 3} 3.621779
{txt}B__orgjust~m {c |}{col 14}{res}{space 2}-1.512988{col 26}{space 2} .3211062{col 37}{space 1}   -4.71{col 46}{space 3}0.000{col 54}{space 4}-2.142345{col 67}{space 3}-.8836316
{txt}B__nonprof~b {c |}{col 14}{res}{space 2} .1496536{col 26}{space 2} .2599888{col 37}{space 1}    0.58{col 46}{space 3}0.565{col 54}{space 4}-.3599151{col 67}{space 3} .6592223
{txt}B__politic~b {c |}{col 14}{res}{space 2}-.6952118{col 26}{space 2} .9320232{col 37}{space 1}   -0.75{col 46}{space 3}0.456{col 54}{space 4}-2.521944{col 67}{space 3}  1.13152
{txt}B__ratio_f~p {c |}{col 14}{res}{space 2} .2471619{col 26}{space 2} .0925472{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0657727{col 67}{space 3} .4285512
{txt}B__ratio_m~p {c |}{col 14}{res}{space 2}-.0362071{col 26}{space 2} .0561357{col 37}{space 1}   -0.64{col 46}{space 3}0.519{col 54}{space 4}-.1462311{col 67}{space 3}  .073817
{txt}B__ratio_5~b {c |}{col 14}{res}{space 2}-.1872407{col 26}{space 2}  .852074{col 37}{space 1}   -0.22{col 46}{space 3}0.826{col 54}{space 4}-1.857275{col 67}{space 3} 1.482794
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.422257{col 26}{space 2} .7269559{col 37}{space 1}   -6.08{col 46}{space 3}0.000{col 54}{space 4}-5.847065{col 67}{space 3} -2.99745
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-3.248416{col 26}{space 2} .1777618{col 54}{space 4}-3.596823{col 67}{space 3} -2.90001
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}a_id        {col 14}{txt}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1479922{col 26}{space 2} .0286403{col 54}{space 4} .1012766{col 67}{space 3}  .216256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay model:model}}{col 14}{c |}{res}{col 16}       897{col 27}        .{col 38} -2738.61{col 49}    29{col 58} 5535.221{col 69} 5674.393
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. 
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECTS BASED ON BETWEEN EFFECTS INTERDECILE CHANGES IN RESPECTIVE COVARIATES ****
. 
. lincom _b[B__ratio_40over_suplb]*0.9427874  - _b[B__ratio_40over_suplb]*0.7818404, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.160947{res}*{res}[age_discrimination]B__ratio_40over_suplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .7280923{col 26}{space 2} .0922863{col 37}{space 1}   -2.50{col 46}{space 3}0.012{col 54}{space 4} .5679315{col 67}{space 3} .9334197
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. lincom _b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.2319127{res}*{res}[age_discrimination]B__ratio_40over_nonsuplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.505955{col 26}{space 2} .3307854{col 37}{space 1}    1.86{col 46}{space 3}0.062{col 54}{space 4} .9791389{col 67}{space 3}  2.31622
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECT ABSOLUTE DIFFERENCE [BASED ON INTERDECILE CHANGES] BETWEEN THESE RESPECTIVE COVARIATES ON THE INCIDENCE OF AGE DISCRIMINATION FORMAL COMPLAINTS  ****
. 
. testnl  abs(_b[B__ratio_40over_suplb]*0.9427874  -  _b[B__ratio_40over_suplb]*0.7818404) = abs(_b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603)

{col 3}{txt}(1){res}{col 8}{bind:abs(_b[B__ratio_40over_suplb]*0.9427874 - _b[B__ratio_40over_suplb]*0.7818404) = abs(_b[B__ratio_40over_nonsuplb]*0.792173 - _b[B__ratio_40over_nonsuplb]*0.5602603)}

{txt}{ralign 22:chi2({res:1})} =  {res}      0.22
{txt}{ralign 22:Prob > chi2} =  {res}      0.6401{txt}

{com}. *
. *
. *
. *
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECT FOR ORGANIZATIONAL JUSTICE  BASED ON BETWEEN INTERDECILE CHANGE IN COVARIATE **
. 
. lincom _b[B__orgjustice_sem]*0.2659021 - _b[B__orgjustice_sem]*-0.1089841, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.3748862{res}*{res}[age_discrimination]B__orgjustice_sem = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  .567112{col 26}{space 2}  .068268{col 37}{space 1}   -4.71{col 46}{space 3}0.000{col 54}{space 4} .4479223{col 67}{space 3} .7180175
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. 
. 
. 
. 
. ** MODEL 3: RATIO OF OVER40 SUPERVISORS TO OVER40 NON-SUPERVISORS [RATIO OF ratio_40over_suplb TO ratio_40over_nonsuplb]: ONLY RANDOM INTERCEPT MODEL SPECIFICATION WITH BE & WE ESTIMATES FOR ALL COVARIATES [SANS YEAR UNIT EFFECTS & LN(TOTAL WORKFORCE)] REDUCED MODEL: ONLY CONTROL COVARIATES ARE LNTEF & YEAR UNIT EFFECTS ***
. 
. xthybrid age_discrimination  ratio40suplb_nonsuplb   lntwf ///
> yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10 ratio_55over_totallb , clusterid(a_id) vce(cluster a_id)  family(nbinomial) link(log) full ///
> use(ratio40suplb_nonsuplb ratio_55over_totallb) 

{res}
{txt}{hline}
{p 0 8}Model {hi:model}{p_end}
{hline}

{txt}Mixed-effects GLM{col 49}{txt}Number of obs{col 67}={res}{col 69}       897
{txt}Family: {col 15}{res}negative binomial
{txt}Link: {col 29}{res}log
{txt}Overdispersion: {col 28}{res}mean
{txt}Group variable: {col 28}{res}a_id{col 49}{txt}Number of groups{col 67}={res}{col 69}       130

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       6.9
{col 63}{txt}max{col 67}={res}{col 69}        10

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}14{txt}){col 67}={res}{col 70}   930.14
{txt}Log pseudolikelihood = {res}-2761.8159{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. Err. adjusted for {res:130} clusters in a_id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}age_discri~n{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}R__yr2 {c |}{col 14}{res}{space 2}-.0194865{col 26}{space 2} .0486626{col 37}{space 1}   -0.40{col 46}{space 3}0.689{col 54}{space 4}-.1148633{col 67}{space 3} .0758904
{txt}{space 6}R__yr3 {c |}{col 14}{res}{space 2}-.0301864{col 26}{space 2} .0574446{col 37}{space 1}   -0.53{col 46}{space 3}0.599{col 54}{space 4}-.1427759{col 67}{space 3}  .082403
{txt}{space 6}R__yr4 {c |}{col 14}{res}{space 2}-.0960094{col 26}{space 2} .0656037{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4}-.2245903{col 67}{space 3} .0325714
{txt}{space 6}R__yr5 {c |}{col 14}{res}{space 2}-.1123844{col 26}{space 2} .0766192{col 37}{space 1}   -1.47{col 46}{space 3}0.142{col 54}{space 4}-.2625552{col 67}{space 3} .0377864
{txt}{space 6}R__yr6 {c |}{col 14}{res}{space 2}-.0298477{col 26}{space 2} .0726104{col 37}{space 1}   -0.41{col 46}{space 3}0.681{col 54}{space 4}-.1721614{col 67}{space 3}  .112466
{txt}{space 6}R__yr7 {c |}{col 14}{res}{space 2} .0224212{col 26}{space 2} .0750958{col 37}{space 1}    0.30{col 46}{space 3}0.765{col 54}{space 4}-.1247639{col 67}{space 3} .1696062
{txt}{space 6}R__yr8 {c |}{col 14}{res}{space 2}-.0803089{col 26}{space 2} .0753665{col 37}{space 1}   -1.07{col 46}{space 3}0.287{col 54}{space 4}-.2280245{col 67}{space 3} .0674067
{txt}{space 6}R__yr9 {c |}{col 14}{res}{space 2}-.0348257{col 26}{space 2} .0795161{col 37}{space 1}   -0.44{col 46}{space 3}0.661{col 54}{space 4}-.1906743{col 67}{space 3} .1210229
{txt}{space 5}R__yr10 {c |}{col 14}{res}{space 2}-.0717933{col 26}{space 2} .0893557{col 37}{space 1}   -0.80{col 46}{space 3}0.422{col 54}{space 4}-.2469273{col 67}{space 3} .1033407
{txt}{space 4}R__lntwf {c |}{col 14}{res}{space 2} .8678683{col 26}{space 2} .0314428{col 37}{space 1}   27.60{col 46}{space 3}0.000{col 54}{space 4} .8062416{col 67}{space 3} .9294949
{txt}W__ratio40~b {c |}{col 14}{res}{space 2}-.3366848{col 26}{space 2} .3383139{col 37}{space 1}   -1.00{col 46}{space 3}0.320{col 54}{space 4}-.9997678{col 67}{space 3} .3263983
{txt}W__ratio_5~b {c |}{col 14}{res}{space 2} 1.884767{col 26}{space 2} .9817358{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0393997{col 67}{space 3} 3.808934
{txt}B__ratio40~b {c |}{col 14}{res}{space 2}-1.380669{col 26}{space 2} .4526757{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-2.267897{col 67}{space 3}-.4934413
{txt}B__ratio_5~b {c |}{col 14}{res}{space 2}-.5690244{col 26}{space 2}  .845677{col 37}{space 1}   -0.67{col 46}{space 3}0.501{col 54}{space 4}-2.226521{col 67}{space 3} 1.088472
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-3.106178{col 26}{space 2} .8706378{col 37}{space 1}   -3.57{col 46}{space 3}0.000{col 54}{space 4}-4.812597{col 67}{space 3}-1.399759
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-3.209665{col 26}{space 2} .1826142{col 54}{space 4}-3.567582{col 67}{space 3}-2.851748
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}a_id        {col 14}{txt}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2046735{col 26}{space 2} .0346679{col 54}{space 4} .1468534{col 67}{space 3}  .285259
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay model:model}}{col 14}{c |}{res}{col 16}       897{col 27}        .{col 38}-2761.816{col 49}    17{col 58} 5557.632{col 69} 5639.216
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. 
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECTS BASED ON BETWEEN EFFECTS INTERDECILE CHANGES IN RESPECTIVE COVARIATES ****
. 
. lincom _b[B__ratio40suplb_nonsuplb]*1.474348 - _b[B__ratio40suplb_nonsuplb]*1.124221, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.350127{res}*{res}[age_discrimination]B__ratio40suplb_nonsuplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6166772{col 26}{space 2} .0977396{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4} .4520095{col 67}{space 3} .8413335
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. *
. *
. 
. 
. ** MODEL 4: RATIO OF OVER40 SUPERVISORS TO OVER40 NON-SUPERVISORS [RATIO OF ratio_40over_suplb TO ratio_40over_nonsuplb]: ONLY RANDOM INTERCEPT MODEL SPECIFICATION WITH BE & WE ESTIMATES FOR ALL COVARIATES [SANS YEAR UNIT EFFECTS & LN(TOTAL WORKFORCE)] --- FULL MODEL: ALL CONTROL COVARIATES ARE LNTEF & YEAR UNIT EFFECTS ***
. 
. xthybrid age_discrimination  ratio40suplb_nonsuplb    orgjustice_sem    nonprof40over_tr_lb     politicization_lb ratio_fsup_msup ratio_minsup_nonmsup lntwf ///
> yr2 yr3 yr4 yr5 yr6 yr7 yr8 yr9 yr10  ratio_55over_totallb , clusterid(a_id) vce(cluster a_id)  family(nbinomial) link(log) full ///
> use(ratio40suplb_nonsuplb   orgjustice_sem  nonprof40over_tr_lb    politicization_lb   ratio_fsup_msup ratio_minsup_nonmsup ratio_55over_totallb) 

{res}
{txt}{hline}
{p 0 8}Model {hi:model}{p_end}
{hline}

{txt}Mixed-effects GLM{col 49}{txt}Number of obs{col 67}={res}{col 69}       897
{txt}Family: {col 15}{res}negative binomial
{txt}Link: {col 29}{res}log
{txt}Overdispersion: {col 28}{res}mean
{txt}Group variable: {col 28}{res}a_id{col 49}{txt}Number of groups{col 67}={res}{col 69}       130

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       6.9
{col 63}{txt}max{col 67}={res}{col 69}        10

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}24{txt}){col 67}={res}{col 70}  1400.73
{txt}Log pseudolikelihood = {res}-2738.1349{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. Err. adjusted for {res:130} clusters in a_id)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}age_discri~n{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}R__yr2 {c |}{col 14}{res}{space 2}-.0096493{col 26}{space 2} .0474076{col 37}{space 1}   -0.20{col 46}{space 3}0.839{col 54}{space 4}-.1025664{col 67}{space 3} .0832678
{txt}{space 6}R__yr3 {c |}{col 14}{res}{space 2}-.0149051{col 26}{space 2} .0538182{col 37}{space 1}   -0.28{col 46}{space 3}0.782{col 54}{space 4}-.1203869{col 67}{space 3} .0905767
{txt}{space 6}R__yr4 {c |}{col 14}{res}{space 2}-.0721719{col 26}{space 2} .0641779{col 37}{space 1}   -1.12{col 46}{space 3}0.261{col 54}{space 4}-.1979582{col 67}{space 3} .0536144
{txt}{space 6}R__yr5 {c |}{col 14}{res}{space 2}-.0802646{col 26}{space 2} .0762641{col 37}{space 1}   -1.05{col 46}{space 3}0.293{col 54}{space 4}-.2297395{col 67}{space 3} .0692104
{txt}{space 6}R__yr6 {c |}{col 14}{res}{space 2} .0064499{col 26}{space 2} .0710582{col 37}{space 1}    0.09{col 46}{space 3}0.928{col 54}{space 4}-.1328217{col 67}{space 3} .1457214
{txt}{space 6}R__yr7 {c |}{col 14}{res}{space 2}  .057878{col 26}{space 2} .0771548{col 37}{space 1}    0.75{col 46}{space 3}0.453{col 54}{space 4}-.0933426{col 67}{space 3} .2090986
{txt}{space 6}R__yr8 {c |}{col 14}{res}{space 2} -.044925{col 26}{space 2} .0798026{col 37}{space 1}   -0.56{col 46}{space 3}0.573{col 54}{space 4}-.2013352{col 67}{space 3} .1114852
{txt}{space 6}R__yr9 {c |}{col 14}{res}{space 2} .0085999{col 26}{space 2} .0819607{col 37}{space 1}    0.10{col 46}{space 3}0.916{col 54}{space 4}-.1520402{col 67}{space 3} .1692399
{txt}{space 5}R__yr10 {c |}{col 14}{res}{space 2}-.0218498{col 26}{space 2} .0923705{col 37}{space 1}   -0.24{col 46}{space 3}0.813{col 54}{space 4}-.2028927{col 67}{space 3} .1591931
{txt}{space 4}R__lntwf {c |}{col 14}{res}{space 2} .8444365{col 26}{space 2} .0315878{col 37}{space 1}   26.73{col 46}{space 3}0.000{col 54}{space 4} .7825255{col 67}{space 3} .9063474
{txt}W__ratio40~b {c |}{col 14}{res}{space 2} -.323847{col 26}{space 2} .4164606{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-1.140095{col 67}{space 3} .4924008
{txt}W__orgjust~m {c |}{col 14}{res}{space 2} .0940162{col 26}{space 2} .2531665{col 37}{space 1}    0.37{col 46}{space 3}0.710{col 54}{space 4}-.4021811{col 67}{space 3} .5902135
{txt}W__nonprof~b {c |}{col 14}{res}{space 2} .1256126{col 26}{space 2} 1.013657{col 37}{space 1}    0.12{col 46}{space 3}0.901{col 54}{space 4}-1.861119{col 67}{space 3} 2.112344
{txt}W__politic~b {c |}{col 14}{res}{space 2}-1.319879{col 26}{space 2} 1.510757{col 37}{space 1}   -0.87{col 46}{space 3}0.382{col 54}{space 4}-4.280908{col 67}{space 3}  1.64115
{txt}W__ratio_f~p {c |}{col 14}{res}{space 2} .1600606{col 26}{space 2} .2352416{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.3010044{col 67}{space 3} .6211255
{txt}W__ratio_m~p {c |}{col 14}{res}{space 2}-.3971245{col 26}{space 2} .0982684{col 37}{space 1}   -4.04{col 46}{space 3}0.000{col 54}{space 4} -.589727{col 67}{space 3}-.2045221
{txt}W__ratio_5~b {c |}{col 14}{res}{space 2} 1.766314{col 26}{space 2} .9817557{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.1578918{col 67}{space 3}  3.69052
{txt}B__ratio40~b {c |}{col 14}{res}{space 2}-1.031335{col 26}{space 2}  .424189{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4}-1.862731{col 67}{space 3}-.1999404
{txt}B__orgjust~m {c |}{col 14}{res}{space 2}-1.588746{col 26}{space 2} .3087411{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-2.193867{col 67}{space 3}-.9836243
{txt}B__nonprof~b {c |}{col 14}{res}{space 2} .1286407{col 26}{space 2} .2632761{col 37}{space 1}    0.49{col 46}{space 3}0.625{col 54}{space 4}-.3873709{col 67}{space 3} .6446523
{txt}B__politic~b {c |}{col 14}{res}{space 2} -.605849{col 26}{space 2} .9444695{col 37}{space 1}   -0.64{col 46}{space 3}0.521{col 54}{space 4}-2.456975{col 67}{space 3} 1.245277
{txt}B__ratio_f~p {c |}{col 14}{res}{space 2} .2524483{col 26}{space 2} .0915941{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0729271{col 67}{space 3} .4319695
{txt}B__ratio_m~p {c |}{col 14}{res}{space 2}-.0383577{col 26}{space 2} .0569977{col 37}{space 1}   -0.67{col 46}{space 3}0.501{col 54}{space 4}-.1500712{col 67}{space 3} .0733558
{txt}B__ratio_5~b {c |}{col 14}{res}{space 2} -.597441{col 26}{space 2} .6922537{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4}-1.954233{col 67}{space 3} .7593513
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-3.474654{col 26}{space 2}  .887498{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-5.214118{col 67}{space 3} -1.73519
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}/lnalpha {c |}{col 14}{res}{space 2}-3.250389{col 26}{space 2}  .177747{col 54}{space 4}-3.598767{col 67}{space 3}-2.902011
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}a_id        {col 14}{txt}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1477306{col 26}{space 2} .0287788{col 54}{space 4} .1008443{col 67}{space 3} .2164162
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay model:model}}{col 14}{c |}{res}{col 16}       897{col 27}        .{col 38}-2738.135{col 49}    27{col 58}  5530.27{col 69} 5659.844
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. 
. 
. *** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECTS BASED ON BETWEEN EFFECTS INTERDECILE CHANGES IN RESPECTIVE COVARIATES ****
. 
. lincom _b[B__ratio40suplb_nonsuplb]*1.474348 - _b[B__ratio40suplb_nonsuplb]*1.124221, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.350127{res}*{res}[age_discrimination]B__ratio40suplb_nonsuplb = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .6969104{col 26}{space 2} .1035051{col 37}{space 1}   -2.43{col 46}{space 3}0.015{col 54}{space 4} .5209026{col 67}{space 3} .9323896
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. 
. 
. ** COMPUTE IRR [EXPONENTIATED] MARGINAL EFFECT FOR PROCEDURAL FAIRNESS BASED ON BETWEEN INTERDECILE CHANGE IN COVARIATE **
. 
. lincom _b[B__orgjustice_sem]*0.2659021 - _b[B__orgjustice_sem]*-0.1089841, eform

{p 0 7}{space 1}{text:( 1)}{space 1} {res}.3748862{res}*{res}[age_discrimination]B__orgjustice_sem = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}age_discri~n{col 14}{c |}     exp(b){col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5512324{col 26}{space 2} .0638012{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4} .4393537{col 67}{space 3} .6916002
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. *
. 
. save "C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination.Post-Estimation.06-02-2022.dta", replace 
{txt}file C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination.Post-Estimation.06-02-2022.dta saved

{com}. 
. 
. *** JUNGYEON: FIGURE 2: COMPOSE VERTICAL GRAPH DISPLAYING POINT ESTIMATES AND CORRESPONDING 95% CI FOR THE ABOVE LINCOMS FROM MODELS 1 & 2 ESTIMATES ///
> ***                     [MODELS 2 & 4 ESTIMATES: 1/1/1] ***
. clear
{txt}
{com}. 
. import excel "C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\figure2.xlsx", sheet("Sheet1") firstrow
{res}{txt}
{com}. destring, replace
{txt}row already numeric; no {res}replace
{txt}group already numeric; no {res}replace
{txt}estimates already numeric; no {res}replace
{txt}low95 already numeric; no {res}replace
{txt}high95 already numeric; no {res}replace
{txt}
{com}. graph set window fontface "Century Schoolbook"
{txt}
{com}. set scheme sj, permanently
{txt}({cmd:set scheme} preference recorded)

{com}. twoway (rcap low95 high95 row, vert) (scatter estimates row if group ==1, mlabel(estimates))(scatter estimates row if group ==2, mlabel(estimates)), legend(row(1) order(2 "Reduced Model" 3 "Full Model") pos(6) size(small)) ylabel(-0.5(.25)2, labsize (small) angle(horizon)) xtitle("% of 'Older' Supervisors(H1)        % of 'Older' Non-Supervisors(H2)        Relative Balance", size(small)) xlabel("", noticks) yline(0, lpattern(dash) lcolor(gs8)) aspect(.5) 
{res}{txt}
{com}. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\jp18390\Dropbox\DISCRIMINATION PROJECT (U.S. FEDERAL AGENCIES)\Age Discrimination Project\PAR R&R\Statistics\Age_Discrimination.MANUSCRIPT.06-02-2022.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 2 Jun 2022, 18:02:44
{txt}{.-}
{smcl}
{txt}{sf}{ul off}